{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ec213466-9b53-4905-8adb-b8a6ed903972",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in c:\\users\\administrator\\anaconda3\\lib\\site-packages (2.17.0)\n",
      "Requirement already satisfied: tensorflow-intel==2.17.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow) (2.17.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (2.1.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=24.3.25 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (24.3.25)\n",
      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.6.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.2.0)\n",
      "Requirement already satisfied: h5py>=3.10.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (3.11.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (18.1.1)\n",
      "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.4.1)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (3.3.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (23.2)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (3.20.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (2.32.2)\n",
      "Requirement already satisfied: setuptools in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (69.5.1)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.16.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (2.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (4.11.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.14.1)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.66.1)\n",
      "Requirement already satisfied: tensorboard<2.18,>=2.17 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (2.17.1)\n",
      "Requirement already satisfied: keras>=3.2.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (3.5.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.26.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.26.4)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.17.0->tensorflow) (0.43.0)\n",
      "Requirement already satisfied: rich in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (13.3.5)\n",
      "Requirement already satisfied: namex in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (0.0.8)\n",
      "Requirement already satisfied: optree in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (0.12.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.17.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.17.0->tensorflow) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.17.0->tensorflow) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.17.0->tensorflow) (2024.7.4)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (3.0.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (2.1.3)\n",
      "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from rich->keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (2.2.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from rich->keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (2.15.1)\n",
      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\administrator\\anaconda3\\lib\\site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (0.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8f4482df-48f6-406a-8276-aee821eb1407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('f(x)=sigmoid(x)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sigmoid(x)')\n",
    "x=np.arange(-10,10,0.1)\n",
    "y=tf.nn.sigmoid(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c732a70f-22ea-4e15-81e4-018919bc3170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('f(x)=Tanh(x)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Tanh(x)')\n",
    "x=np.arange(-10,10,0.1)\n",
    "y=tf.nn.tanh(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c7c2497a-ae1b-47e6-a4a6-bce0588f72e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('f(x)=ReLU(x)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Relu(x)')\n",
    "x=np.arange(-10,10,0.1)\n",
    "y=tf.nn.relu(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "60a77128-e1d1-41e8-9b4e-826df8c0e968",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "softmax([1,1,5,2,3,2])= [0.01440429 0.01440429 0.7864475  0.03915492 0.1064341  0.03915492]\n",
      "sum(softmax([1,1,5,2,3,2]))= 1.0000000335276127\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "x=tf.constant([1,1,5,2,3,2],tf.float32)\n",
    "y=tf.nn.softmax(x)\n",
    "print(\"softmax([1,1,5,2,3,2])=\",y.numpy())\n",
    "print(\"sum(softmax([1,1,5,2,3,2]))=\",sum(y.numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f60879bf-f6cf-4dc0-8009-1805b141452d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
